A method and system for distributing reliability indicators for an electrically driven turnout rammer based on a substance-energy-information network

Abstract

This invention relates to the field of reliability engineering technology, specifically to a method and system for allocating certainty reliability indicators for an electrically driven turnout tamping machine based on a material-energy-information network. The method first performs functional decomposition and performance analysis of the entire machine, extracting key performance parameters of each subsystem and constructing performance margins. Based on certainty reliability theory, it determines the material-related reliability. Secondly, it establishes energy and information networks through the electric traction power supply relationship and the telemetry and control link, respectively, calculating the energy-related reliability and information-related reliability of each subsystem, and combining these three to obtain the subsystem functional reliability. Based on this, the system reliability is expressed as the minimum value of the subsystem functional reliability. System reliability interval constraints and performance margin mean and variance constraints are introduced to construct an optimization model with causal importance-weighted subsystem functional reliability as the objective function. An intelligent optimization algorithm is used to solve for the reliability indicators and margin allocation of each subsystem. This method can achieve refined and quantitative allocation of reliability indicators for each subsystem of an electrically driven turnout tamping machine under small sample and uncertain conditions, improving the reliability and safety of the entire machine under complex operating conditions.

Description

Technical Field

[0001] This invention relates to the field of reliability engineering technology, specifically to a method and system for allocating reliability indicators for an electrically driven turnout tamping machine based on a material-energy-information network. It is particularly applicable to the reliability indicator allocation and optimization design of railway line maintenance equipment that includes multiple subsystems such as an electric traction system, a tamping operation system, and a measurement and control system. Background Technology

[0002] As a key maintenance equipment in high-speed and heavy-haul railway turnout sections, the electric turnout tamping machine needs to complete a series of coordinated actions during operation, including track lifting and shifting, tamping, stabilization, measurement and control, and multi-source power supply. To ensure the quality of track geometry and the safety of turnout structure, the entire vehicle must have a high level of reliability under specified operating conditions and within its life cycle. As turnout structures become more complex, operating environments become harsher, and the integration of systems such as electric traction and electrical control continues to increase, traditional reliability index allocation methods that rely on empirical coefficients or simple weighted sums based on failure rates are insufficient to fully reflect the performance degradation, uncertainty, and coupling relationships of each subsystem during the design phase, leading to a mismatch between the allocation results and actual usage risks.

[0003] To achieve high reliability and operational stability of the new electric turnout tamping machine, reliability indicators need to be allocated during the initial design phase, thereby enabling control over the reliability levels of each subsystem. ] Reasonable reliability allocation not only significantly improves the economy and safety of a system, but also plays a crucial role in reliability optimization design. Currently used methods include equal allocation, reallocation, proportional allocation, and comprehensive scoring methods. However, in engineering practice, due to the complex structure and high degree of interdisciplinary collaboration of turnout tamping machines, the various subsystems are tightly coupled through power supply and information transmission, exhibiting strong interdependence. Most traditional methods still assume the system is a series system composed of independent components, leading to insufficient reliability and rationality of the allocation results. Secondly, existing methods mainly target reliability or mean time between failures (MTBF), and the evaluation standards and methods used when allocating these indicators to various subsystems are inconsistent, hindering the effective integration of reliability requirements into functional and performance design and reducing the engineering guidance value. Thirdly, due to the advanced technology of new electric turnout tamping machines, there is a lack of mature similar products for reference, and related technical data is incomplete. Therefore, when conducting reliability assessments of new electric turnout tamping machines, the inherent scoring process of traditional reliability allocation methods is difficult to implement objectively and accurately.

[0004] In existing technologies, various studies have been conducted on the allocation of reliability indicators for complex systems. For example, Chinese patent document CN107748808A proposes a "reliability indicator allocation optimization method, system, and medium based on interval constraints." This method compares the system's predicted reliability indicators with the design indicators to determine whether optimization is needed. It also comprehensively considers factors such as system complexity, technological maturity, importance, environmental conditions, and mission time to construct weight parameters. Based on given interval constraints on the reliability indicators of subsystems, it establishes a reliability indicator allocation optimization model to achieve comprehensive trade-off optimization of reliability indicators for systems such as aerospace models. This method can consider the impact of multiple factors on allocation within a probabilistic reliability framework. However, it uses failure rate and traditional reliability as core quantitative indicators, fails to characterize the functional degradation process from the perspective of performance margin, and does not introduce the correlation between energy and information. Its adaptability to small sample and high uncertainty scenarios is limited.

[0005] With the development of the theory of certainty reliability, several allocation methods based on performance margin and certainty reliability have been proposed by academia. For example, the paper "A Certainty Reliability Allocation Method Based on Technology Maturity" (Systems Engineering and Electronics, Document No.: 10.12305 / j.issn.1001-506X.2022.01.41) proposes to take uncertain stochastic systems as the object, use certainty reliability as the core metric, classify system units using technology maturity, construct a reliability opportunity cost function, establish an optimal allocation model and solution algorithm for system certainty reliability, and verify the effectiveness of the method on a ship dual-fuel gas supply system. This type of method can better handle parameter uncertainty under small sample conditions and introduces certainty reliability into the allocation process. However, it mainly weighs costs and technology maturity, and still remains at the level of abstract "unit reliability index". It has not yet established a unified model for the causal relationship between specific energy transfer networks, information interaction networks and key performance parameters, and has not considered the comprehensive impact of multi-source power supply and control link failure on the overall reliability of railway maintenance equipment.

[0006] On the other hand, existing technologies have proposed reliability modeling and allocation approaches based on information networks to address the information interaction mechanisms of complex electromechanical systems. For example, Chinese patent document CN118261071A (application number: 202410692663.8) discloses an "information network model and reliability calculation method for motor reliability allocation." This method constructs an information network model of the motor system, introduces the concept of information-related reliability, models the uncertainties in the information interaction process, and combines the functional reliability of the subsystems themselves to achieve reliability allocation and calculation for the motor system. While this technology has some reference value in modeling the impact of information interaction on reliability, it is geared towards motor systems and does not introduce the theory of assured reliability and the concept of performance margin. Furthermore, it does not simultaneously address the dual correlation between energy networks and information networks, nor does it combine this with multi-subsystem, multi-condition coupled equipment such as railway turnout tamping machines to conduct system-level assured reliability optimization allocation.

[0007] In summary, existing technologies still have shortcomings in the following aspects:

[0008] 1. Traditional index allocation methods based on failure rate and probabilistic reliability mostly use "reliability value" or "failure rate" as the only measure, which makes it difficult to describe the degradation process of each subsystem of the turnout tamping machine from the chain of function-performance-performance margin, and cannot fully reflect the uncertainty under small sample conditions.

[0009] 2. There are existing allocation methods based on certainty reliability. Although performance margin and certainty reliability are introduced into the model, they usually only weigh macro factors such as technology maturity and cost. They lack a detailed characterization of the energy transfer relationship and information interaction structure of the system, and a certainty reliability allocation model under the three-layer relationship of matter, energy and information has not yet been established.

[0010] 3. In the railway sector, existing reliability allocation methods are mostly focused on traction systems, braking systems, or general rail transit vehicles. For railway infrastructure maintenance equipment such as electric turnout tamping machines, there is no known method for allocating reliability indicators that integrates performance margin, energy-related reliability, information-related reliability, and system reliability interval constraints. This makes it difficult to support the refined and quantitative allocation of reliability indicators for each subsystem at the vehicle-level design stage.

[0011] Therefore, it is necessary to propose a reliability index allocation method for electric turnout tamping machines. This method introduces the theory of certainty reliability into the field of railway maintenance equipment, constructs the material-related reliability of subsystems based on performance margins, and obtains energy-related reliability and information-related reliability by combining energy networks and information networks. Under the constraints of system reliability intervals and the mean and variance constraints of performance margins, the method achieves a reasonable allocation of reliability indexes for each subsystem through optimization, thereby improving the overall reliability and safety of electric turnout tamping machines under complex working conditions. Summary of the Invention

[0012] To address the aforementioned technical problems, the present invention aims to provide a reliable index allocation method for electrically driven turnout tamping machines based on a material-energy-information network. This method, considering the coupling relationships between subsystems, rationally allocates the mean and variance of the performance margins of each subsystem. The system dependencies are characterized using a material-information-energy ternary network. Within this network, a causal importance index is introduced to quantify the criticality of each subsystem and serves as a weighting coefficient for constructing the reliability allocation optimization model. This method contributes to achieving a more objective and reasonable reliability allocation.

[0013] To achieve the above objectives, the present invention adopts the following technical solution:

[0014] A method for allocating reliability indicators for an electrically driven turnout tamping machine based on a matter-energy-information network includes the following steps:

[0015] S1, Establish a subsystem reliability decomposition model: Divide the electrically driven turnout tamping machine into several subsystems, and then... Establish material correlation reliability for each subsystem Its probability of a performance margin being greater than zero is defined as follows:

[0016] ;

[0017] in, For the first Material correlation reliability of the subsystem; For the first Normalized performance margin of the subsystem; Represents a probability operator;

[0018] And establish the first Functional reliability of the subsystem The product of energy correlation reliability, information correlation reliability, and material correlation reliability:

[0019] , ;

[0020] in, For the first Functional reliability of the subsystem; For the first Energy correlation reliability of the subsystem; For the first Information association reliability of subsystems; For the first Material correlation reliability of the subsystem; Number of subsystems;

[0021] S2, Establish system reliability interval constraints: Set the system reliability interval... Defined as the minimum functional reliability of each subsystem, and limited to a preset threshold range:

[0022] , ;

[0023] in, The system reliability of the electrically driven turnout tamping machine; This is the minimum functional reliability among all subsystems; This is the lower limit threshold for system reliability. This is the upper limit threshold for system reliability. For the first Functional reliability of the subsystem;

[0024] S3, Establish statistical constraints on performance margin: Based on the randomness of performance margin, impose constraints on the mean and variance of the performance margin of each subsystem, specifically:

[0025] ,

[0026] in, For the first Subsystem performance margin The mathematical expectation; For the first Lower limit of the mean performance margin of the subsystem;

[0027] ,

[0028] in, For the first Subsystem performance margin The variance; This serves as an upper limit control indicator for the uncertainty of performance margin;

[0029] S4, Constructing an optimization objective function based on causal importance: Determining the causal importance of each subsystem. The optimization objective is the sum of the functional reliability of subsystems weighted by causal importance.

[0030] ,

[0031] in, This is a weighted sum of the causal importance and functional reliability of each subsystem; For the first Causal importance of subsystems; For the first Functional reliability of the subsystem; This indicates finding the maximum value of the above weighted sum under given constraints;

[0032] S5. Under the premise of satisfying the system reliability constraints and performance margin statistical constraints described in steps S2 and S3, the optimization objective described in step S4 is solved by using an intelligent optimization algorithm to obtain the allocation results of the performance margin and corresponding reliability of each subsystem, and the allocation results are used as the reliability index allocation scheme of each subsystem of the electric drive turnout tamping machine.

[0033] Preferably, the energy correlation reliability The satisfaction probability of the voltage and power dimensions is obtained by taking the smaller of the two probabilities, where the energy correlation reliability of the voltage dimension is... Energy-related reliability in power dimension They are defined as follows:

[0034] ,

[0035] in, The probability of meeting the subsystem requirements in the voltage dimension; This represents the uncertainty situation in the sample space; In the case of Under the following voltage conditions; In the case of The set of conditions that allow for the lower voltage;

[0036] in, The probability of meeting the subsystem requirements in the power dimension; In the case of Under the following power conditions; In the case of The set of conditions that allow for lower power;

[0037] Overall energy correlation reliability Take the smaller of the two values ​​above:

[0038] ,

[0039] in, This indicates that the smaller of the two probabilities of satisfying voltage and power is taken as the conservative reliability assessment at the energy level.

[0040] Preferably, the information association reliability The failure rate parameters are obtained by combining the failure rates of different failure levels. in, The failure rate corresponds to the extremely low failure level, in units of ; The failure rate corresponding to the low failure level; The failure rate corresponding to a medium failure level;

[0041] Information correlation reliability Number of failure modes at each level The combined calculation is as follows:

[0042] ,

[0043] in, For comprehensive reliability at the information level; The number of failure modes classified as extremely low failure levels; The number of failure modes classified as low failure levels; The number of failure modes classified as medium failure level; , , These represent the combined index terms for each level of failure rate.

[0044] Preferably, the causal importance Based on the impact of each subsystem on the overall reliability reduction Normalization yields the following, defined as:

[0045] ,

[0046] in, For the first Causal importance of subsystems; For the first The total decrease in the functional reliability of all subsystems caused by a decrease in the material-related reliability of a subsystem; For all subsystems The maximum value is used for normalization; This indicates that the influence quantity is normalized to The interval is used to obtain the relative importance.

[0047] As a preferred option, when calculating the importance of causality, the first... The subsystem at time New material correlation reliability For the original reliability of ,Right now:

[0048] ,

[0049] in, For at any time After reduction Subsystem material correlation reliability; For at any time Material correlation reliability without reduction; The preset reliability reduction factor is greater than ;

[0050] Define the first by the following formula Subsystem and a certain reference subsystem relative change factor :

[0051] ,

[0052] in, For the first Subsystem relative to reference subsystem The relative change factor; In the first The average performance margin of the subsystem is from Reduced to Reliability of new material correlations under certain circumstances; For the reference subsystem The average performance margin is from Downgraded to Reliability of new material correlations under certain circumstances; For the first Mean performance margin of subsystem; This represents the average performance margin of the baseline subsystem.

[0053] Preferably, the optimization objective The solution employs particle swarm optimization, genetic algorithm, or other swarm intelligence optimization algorithms, and satisfies... as well as The solution set is taken as the feasible region.

[0054] Preferably, when using the particle swarm optimization algorithm, the vectors representing the mean and variance of the performance margin of each subsystem are used as the positions of the particles, and the objective function is applied. The calculated value is used as the particle fitness. Within the feasible region that satisfies the performance margin and system reliability constraints, the particle velocity and position are updated based on individual optimality and swarm optimality. The process is iterated until convergence to obtain an approximate optimal allocation scheme for the performance margin and reliability index of each subsystem.

[0055] Furthermore, the present invention also provides a turnout tamping machine reliability index allocation system for implementing the method, the system comprising:

[0056] The data acquisition and modeling module is used to acquire the structural information, functional requirements and key performance parameters of each subsystem of the turnout tamping machine, convert the key performance parameters into normalized performance margins, establish a function-performance-margin model, and construct energy network and information network to form a material-energy-information ternary correlation model.

[0057] The correlation reliability calculation module is used to calculate the energy correlation reliability and information correlation reliability of each subsystem based on the energy network and information network, and to calculate the material correlation reliability of each subsystem based on the performance margin uncertainty, thereby obtaining the functional reliability of each subsystem;

[0058] The causal importance analysis module is used to calculate the overall reliability decrease and normalize it under the ternary correlation model by reducing the material correlation reliability of the target subsystem and comparing the functional reliability of each subsystem before and after the adjustment, thereby obtaining the causal importance of each subsystem.

[0059] The reliability allocation optimization module is used to construct a reliability index allocation optimization model based on causal importance and functional reliability. Under the constraints of performance margin and system reliability, it uses the particle swarm optimization algorithm to solve the problem and outputs the allocation results of performance margin and reliability index for each subsystem.

[0060] Furthermore, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the method.

[0061] Furthermore, the present invention also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the method.

[0062] This invention introduces the concepts of certainty reliability and performance margin, and performs unified modeling and index allocation for electric turnout tamping machines across three dimensions: material, energy, and information. Compared to allocation methods based solely on failure rate or traditional reliability, this invention, on the one hand, more realistically reflects the safety margins and degradation trends of key performance parameters of each subsystem under small sample and parameter uncertainty conditions, significantly improving the consistency between allocation results and actual failure risks. On the other hand, it characterizes the degree to which the electric traction system meets the power supply capacity of each operating subsystem through energy-related reliability, and characterizes the support capability of the measurement and control link for the operation process through information-related reliability. Combined with the causal importance defined by "reduction margin - reliability reduction" and an optimization model with system reliability interval constraints, performance margin mean and variance constraints, key subsystems that have a greater impact on the overall reliability of the machine automatically obtain higher reliability indicators and margin configurations. This enables quantitative and refined allocation of reliability resources for each subsystem during the overall machine design stage, reducing the risk of over-design or under-configuration, and improving the overall reliability and operational safety of electric turnout tamping machines under complex working conditions. Attached Figure Description

[0063] Figure 1 Flowchart of confidence-based reliability allocation based on performance margin and causal importance.

[0064] Figure 2 Energy network (electric traction subsystem function) of turnout tamping machine.

[0065] Figure 3 The information network of the turnout tamping machine (driven by the electrical subsystem).

[0066] Figure 4 Flowchart of the particle swarm optimization algorithm for solving the performance margin allocation model.

[0067] Figure 5 Normalized results of causal importance of each subsystem. Detailed Implementation

[0068] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the protection scope of the present invention.

[0069] An electrically driven turnout tamping machine is a typical complex system, composed of multiple subsystems encompassing mechanical, electrical, and power transmission fields. These subsystems are coupled and operate collaboratively to achieve the core functions of the equipment. Despite the system's complex structure, the interaction between subsystems is essentially achieved through energy transfer and information transmission mechanisms, ultimately affecting the actuators or functional modules of each subsystem. For any subsystem within the tamping machine, its functional realization hinges on whether it receives sufficient energy for normal operation and whether it has the capability to receive, process, and respond to data streams. More importantly, the stability of this energy and data signals is constrained by the reliability of upstream subsystems, thus forming a cascading chain of dependencies within the system.

[0070] Based on the above understanding, to ensure that the electric turnout tamping machine completes its corresponding functions, three key elements need to be considered. First, the effectiveness of energy input: if the input energy meets the subsystem's requirements, the energy transmission can be considered reliable. The probability that the subsystem's energy input meets its own requirements is called the energy-related reliability, denoted as ρ. Second, the effectiveness of information transmission: if the information sending and receiving subsystems do not experience serious interruptions during communication, the information transmission can be considered reliable. The probability that no faults occur at the subsystem's information level is called the information-related reliability, denoted as χ. Third, considering that even if energy and information inputs are normal, whether the subsystem can achieve its function is still affected by its design, manufacturing, and usage factors, there is also uncertainty. A design reliability under normal energy and information input conditions is introduced, denoted as r. To ensure that reliability allocation can provide improved guidance for the product's functional and performance design process, this application uses a certainty reliability metric to characterize the r of each subsystem of the tamping machine. i , which is represented as

[0071] (1.1)

[0072] Among them, M i This represents the normalized performance margin of the key performance parameter of the i-th subsystem. If a subsystem contains multiple key performance parameters, then each performance margin must meet the assigned requirements.

[0073] Therefore, the functional reliability of each subsystem can be described as follows:

[0074] (1.2)

[0075] Among them, R i ρ i , χ i With r i Let represent the reliability of the i-th subsystem, the energy-related reliability, the information-related reliability, and the material-related reliability, respectively; n is the number of subsystems. In the reliability allocation problem, the core parameter to be allocated is r.i This parameter characterizes the inherent reliability capability of the subsystem's own design, that is, the design reliability.

[0076] Furthermore, subsystems are interconnected through energy transfer and information exchange. Different subsystems have varying degrees of importance, and there is a causal relationship between energy and information; this application categorizes this as causal importance. For example, a subsystem responsible for power distribution interacts with more downstream subsystems, and its reliability has a more significant impact on other subsystems, thus possessing higher causal importance and should be assigned a higher reliability index. Based on this, the assured reliability allocation of the novel electrically driven turnout tamping machine can be described as: under the premise of meeting the overall system reliability requirements, allocating the highest possible reliability index to subsystems with higher causal importance. Therefore, the assured reliability allocation process can be described as an optimization problem.

[0077] Based on the above principles, this application proposes a novel reliability index allocation process for electrically driven turnout tamping machines based on assured reliability (see...). Figure 1 It mainly includes three steps:

[0078] (1) Perform functional, performance and margin analysis on the tamping machine, clarify the system structure and functional mechanism, construct energy correlation network and information correlation network accordingly, and analyze the correlation between network elements.

[0079] (2) Based on the above correlation model, analyze the impact of subsystem reliability changes on the overall system reliability, determine the causal importance of each subsystem, and use this as the weight to construct a confidence reliability allocation optimization model.

[0080] (3) Solve the optimized allocation model to obtain the performance margin allocation results for each key subsystem. Then, conduct system reliability verification to determine whether the optimized allocation results meet the system reliability index requirements.

[0081] I. Matter-Energy-Information Correlation Modeling

[0082] A. Determination of Key Performance Parameters

[0083] Key performance parameters (KPPs) are performance parameters that can quantitatively characterize a product's ability to achieve a specific key function. Their identification can be based on functional, performance, and margin analysis methods. An electrically driven turnout tamping machine consists of 14 subsystems: tamping subsystem, track lifting and shifting subsystem, measurement subsystem, electric traction subsystem, power subsystem, cooling subsystem, backfilling subsystem, sleeper surface cleaning subsystem, braking subsystem, electrical subsystem, running gear subsystem, frame subsystem, driver's cab subsystem, and coupler and traction device subsystem.

[0084] When identifying key performance parameters through functional, performance, and margin analysis, two main factors are considered: first, the severity of the consequences of exceeding tolerances, i.e., the impact of functional loss on the basic usability of the product when performance requirements are not met; second, the degree of user attention, i.e., the user's demand and requirements for the product's related performance. Ultimately, nine key performance parameters for the tamping machine were selected, and their corresponding functions and subsystems are shown in Table 1.

[0085] Table 1 Key Performance Parameters

[0086]

[0087] B. Establishing Network Relationships

[0088] The energy network of the turnout tamping machine is crucial, ensuring that other subsystems receive the electrical energy required for normal operation, such as... Figure 2 As shown, the arrows indicate the direction of energy transfer. This network uses the electric traction subsystem as its central power source, efficiently distributing energy to each subsystem and achieving effective management through a control and information network. A correct understanding and optimization of this network is crucial for improving the overall performance and reliability of the system.

[0089] The turnout tamping machine is controlled by an electrical subsystem. Various subsystems interact with the electrical subsystem via control modules and sensors to achieve rapid and accurate transmission and control of information. Based on the internal network structure and information relationships of the tamping machine, the system's information network is constructed as follows: Figure 3 The arrow indicates the direction of information transmission.

[0090] C. System-related reliability calculation

[0091] 1. Energy correlation reliability

[0092] When calculating energy correlation reliability, it is necessary to consider the performance parameters of both the input voltage and power of the energy network simultaneously. That is, it is necessary to calculate the probability that the input voltage of each subsystem meets the requirements. And the probability that the input power meets the requirements. , respectively represented as:

[0093] (1.3)

[0094] in This can be expressed as possible values ​​for voltage or power. and represent the voltage and power output values ​​of the electric traction subsystem, respectively; and represent the requirements for the input voltage and power of the subsystem, respectively.

[0095] Since both output voltage and power are affected by the reliability of the electric traction subsystem, and whether the two meet the requirements is not an independent event, based on the uncertainty theory proposed by Professor Liu Baoding, the smaller principle is used to evaluate the energy-related reliability. :

[0096] (1.4)

[0097] 2. Information correlation reliability

[0098] Based on the established information network relationships, the potential failure modes and their levels of the turnout tamping machine at the information level are identified, and the failure probability of each subsystem is assessed to calculate the information association reliability χ. Due to the scarcity of overall machine failure data, it is difficult to effectively quantify the occurrence probability of each subsystem failure mode; at this stage, only qualitative failure levels can be used. The extremely low, low, and medium failure levels are respectively mapped to the following specific failure rates: Further assuming that the time of failure at the information layer follows an exponential distribution, and combining the failure rate with the task time, the resulting formula is:

[0099] (1.5)

[0100] in These represent the number of failure modes categorized into the very low, low, and medium levels, respectively.

[0101] II. Reliability Index Allocation Model Based on Causal Importance Algorithm

[0102] Based on the system's material-energy-information correlation model, and combined with the given system reliability index requirements, as well as the degradation and uncertainty constraints of subsystem performance margins, a system performance margin allocation model is established.

[0103] A. Causal Importance Algorithm

[0104] To achieve a reasonable allocation of performance margins for subsystems, this application uses causal importance to measure the impact of subsystem reliability changes on the reliability of all other subsystems. Specifically, the causal importance of a subsystem characterizes the degree of decrease in the reliability of the subsystem relative to the energy it supplies after the subsystem's design margin has decreased by a certain percentage compared to the original margin. The more significant the decrease, the greater the impact of the subsystem on other subsystems, and the higher its causal importance. The causal importance v of subsystem k... k The calculation can be written as

[0105] (1.6)

[0106] In the formula, v k Depend on Obtained through normalized calculation; This indicates that the material correlation reliability of subsystem k at time T0 is determined by... Down to At that time, the total decrease in the reliability level of all subsystems.

[0107] B. Objective function for reliability index allocation

[0108] B.1 Objective Function

[0109] Based on the aforementioned performance margin analysis and causal importance assessment results, the causal importance v of each subsystem can be obtained. k With reliability R k Therefore, under the premise of satisfying the overall system constraints, higher reliability indices should be allocated to subsystems with higher causal importance as much as possible. The corresponding objective function of the allocation model is:

[0110] (1.7)

[0111] Therefore, the decision variables in this allocation model are the causal importance and reliability of each subsystem.

[0112] B.2 Constraints

[0113] This application constructs a system performance margin allocation model for a turnout tamping machine, and includes the following four types of constraints:

[0114] (1) System Matter-Energy-Information Relationship Model. This model describes the functional and reliability relationships between subsystems under the interaction of matter, energy and information.

[0115] (2) Mean constraint of system performance margin. The average performance margin must satisfy... This means that when deploying and using the system, the performance margin cannot be too low; otherwise, the system will lack the ability to withstand uncertainties during operation. Threshold The determination of the margin requires comprehensive consideration of the engineering constraints and task requirements of the subsystem, and evaluation of the expected average performance margin.

[0116] (3) Uncertainty Constraints on Subsystem Performance Margins. Due to cost and technical limitations, the performance margins of each subsystem are uncertain. This application characterizes this uncertainty using variance and constrains it as follows: Threshold The settings need to be combined with the engineering constraints and task requirements of the subsystem to ensure that the uncertainty of the expected performance margin is within an acceptable range.

[0117] (4) System Reliability Constraints. System reliability is an ideal characteristic parameter, requiring it to exceed a minimum threshold to meet reliability requirements and remain below a maximum threshold to avoid unnecessary costs. Considering all relevant reliability factors, the system's ability to perform its functions depends on the most challenging critical function; therefore, the reliability of this critical function is selected as the system reliability constraint.

[0118] (1.8)

[0119] B.3 Performance Margin Optimization Allocation Model

[0120] After clarifying the optimization objective and the above constraints, a margin allocation model for the turnout tamping system based on performance margin and causal importance can be obtained:

[0121] (1.9)

[0122] C. Solving the allocation model

[0123] The turnout tamping machine consists of multiple subsystems, and its performance margin allocation model is relatively complex. This application uses the particle swarm optimization (PSO) algorithm to obtain an approximate optimal solution under constraints. The detailed calculation steps are shown in Figure 4.

[0124] In PSO, each abstract particle has two types of attributes: velocity and position. Velocity represents the rate of movement, and position represents the direction and coordinates in the search space. Each particle independently searches for and records its current optimal solution, which is then recorded as its current individual optimal value. This information is then shared within the swarm to update and identify new individual optimalities. The best individual optimality in the swarm serves as the current estimate of the global optimality, driving the entire particle swarm to converge toward a better solution.

[0125] All particles continuously adjust their velocity and position based on their individual optimality and the global optimality, thereby continuously optimizing the search process and updating the global optimality. In this study, the parameters of PSO include causal importance v. k Energy correlation reliability ρ k Reliability of information association χ k The variable boundaries are defined by the range of values ​​for the mean and variance of the performance margin. By iteratively updating the particle's velocity and position, the optimized mean performance margin of each subsystem can finally be obtained. With variance .

[0126] III. Case Studies

[0127] This application uses the reliability allocation process of a novel electrically driven turnout tamping machine as an example to verify the effectiveness of the proposed method. During the preparation phase, structural information, product requirements, and expert evaluations of the performance analysis of the equipment were obtained. The case study will utilize this information to demonstrate the analysis and solution process of the reliability of key subsystems, causal importance, and allocation model, ultimately optimizing the reliability allocation. Based on the operating requirements of the tamping machine, the overall machine reliability index is set to be greater than 0.97, and the following allocations are all carried out around this index.

[0128] A. System-related reliability solution

[0129] For turnout tamping machines, the system-level functionality mainly relies on two types of functional principles (see...). Figure 2 and Figure 3 Taking the electric traction subsystem as an example, based on the system energy flow principle and the parameter information provided by the R&D department, initial parameter assumptions are made (see Table 2 for details). Driven by the electric traction subsystem, the energy-related reliability of the electric traction subsystem, electrical subsystem, running subsystem, tamping subsystem, starting and shifting subsystem, and braking subsystem can be calculated separately. Example results are shown below:

[0130] (1.10)

[0131] Table 2 Simulation Parameter Settings

[0132]

[0133] Regarding the calculation of information correlation reliability: Assuming the mean time between failures (MTBF) of the entire system is 200 hours, and under continuous operation for 6 hours, the overall design reliability R is 0.97. This is based on failure rate parameters corresponding to different failure levels. The information-related reliability of the electrical subsystem is approximately 0.99996, and that of the other subsystems is approximately 0.99998.

[0134] B. Causal Importance Analysis

[0135] When calculating the causal importance of a subsystem, the mean value of the original design performance margin of each subsystem is set. ,variance Assuming the performance margin follows a normal distribution, r is obtained by adjusting the material correlation reliability. k =0.9995, and the margin is reduced to 0.9. Based on the above parameters, when the electric traction subsystem is used as the benchmark, the causal importance of each subsystem of the turnout tamping machine can be calculated (see...). Figure 5 ).

[0136] C. Assignment Model Parameter Setting and Calculation Results

[0137] The parameter settings for the allocation model in this case are shown in Table 3. Solving the allocation model based on the above assumptions yields the performance margin and reliability allocation results for each subsystem (see Table 4). The results show that the key performance parameters corresponding to each subsystem must meet the requirements of the normalized performance margin. Taking the electric traction subsystem as an example, its key performance parameter is the traction motor power. Under the constraints of the system reliability index, the mean of the normalized performance margin of this parameter must not be lower than 0.9756, and the variance must not exceed 0.1605.

[0138] As shown in Table 4, the overall reliability of the machine meets the established performance margin allocation constraints. At the same time, the results also reflect that units such as the electrical subsystem, which significantly impact other subsystems, require higher reliability indicators to ensure the stable operation and performance of the entire machine.

[0139] Table 3. Allocation Model Parameter Settings

[0140]

[0141] Table 4 Reliability of each subsystem of the turnout tamping machine

[0142]

[0143] IV. Conclusion

[0144] This application, based on an engineering case, employs margin analysis and causal importance as tools to complete a systematic assurance reliability allocation for a turnout tamping machine. The analysis follows a structured approach: functional analysis, performance analysis, margin analysis, and causal importance analysis are performed sequentially on the complex system. Based on this, a reliability optimization allocation model is constructed, and the feasibility of the method is verified using an electrically driven turnout tamping machine as an example. The main contributions are as follows:

[0145] (1) A reliability optimization allocation method based on certain reliability is proposed to address the reliability allocation requirements of a complex system—a turnout tamping machine.

[0146] (2) By combining performance margin with reliability allocation, the structural characteristics and performance properties of the research object are fully considered, ensuring that the allocation results meet the margin requirements of key performance parameters, and providing a more guiding basis for product development and component management.

[0147] (3) By performing a certainty reliability allocation on a turnout tamping machine, the effectiveness of the reliability allocation method for complex systems based on performance margin and causal importance was verified, thus providing a reference for the reliability allocation of other complex systems.

[0148] The foregoing description of embodiments of the present invention, through which those skilled in the art are able to implement or use the present invention, will be readily apparent to those skilled in the art. Various modifications to these embodiments will be readily apparent to those skilled in the art. The general principles defined in this application may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown in this application, but is to be accorded the widest scope consistent with the principles and novelty disclosed in this application.

[0149] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0150] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0151] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0152] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0153] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0154] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0155] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

Claims

1. A method for allocating reliability indicators for an electrically driven turnout tamping machine based on a matter-energy-information network, characterized in that, Includes the following steps: S1, Establish a subsystem reliability decomposition model: Divide the electrically driven turnout tamping machine into several subsystems, and then... Establish material correlation reliability for each subsystem Its probability of a performance margin being greater than zero is defined as follows: ； in, For the first Material correlation reliability of the subsystem; For the first Normalized performance margin of the subsystem; Represents a probability operator; And establish the first Functional reliability of the subsystem The product of energy correlation reliability, information correlation reliability, and material correlation reliability: ， ； in, For the first Functional reliability of the subsystem; For the first Energy correlation reliability of the subsystem; For the first Information association reliability of subsystems; For the first Material correlation reliability of the subsystem; Number of subsystems; S2, Establish system reliability interval constraints: Set the system reliability interval... Defined as the minimum functional reliability of each subsystem, and limited to a preset threshold range: ， ； in, The system reliability of the electrically driven turnout tamping machine; This is the minimum functional reliability among all subsystems; This is the lower limit threshold for system reliability. This is the upper limit threshold for system reliability. For the first Functional reliability of the subsystem; S3, Establish statistical constraints on performance margin: Based on the randomness of performance margin, impose constraints on the mean and variance of the performance margin of each subsystem, specifically: ， in, For the first Subsystem performance margin The mathematical expectation; For the first Lower limit of the mean performance margin of the subsystem; ， in, For the first Subsystem performance margin The variance; This serves as an upper limit control indicator for the uncertainty of performance margin; S4, Constructing an optimization objective function based on causal importance: Determining the causal importance of each subsystem. The optimization objective is the sum of the functional reliability of subsystems weighted by causal importance. ， in, This is a weighted sum of the causal importance and functional reliability of each subsystem; For the first Causal importance of subsystems; For the first Functional reliability of the subsystem; This indicates finding the maximum value of the above weighted sum under given constraints; S5. Under the premise of satisfying the system reliability constraints and performance margin statistical constraints described in steps S2 and S3, the optimization objective described in step S4 is solved by using an intelligent optimization algorithm to obtain the allocation results of the performance margin and corresponding reliability of each subsystem, and the allocation results are used as the reliability index allocation scheme of each subsystem of the electric drive turnout tamping machine.

2. The method as described in claim 1, characterized in that, The energy correlation reliability The satisfaction probability of the voltage and power dimensions is obtained by taking the smaller of the two probabilities, where the energy correlation reliability of the voltage dimension is... Energy-related reliability in power dimension They are defined as follows: ， in, The probability of meeting the subsystem requirements in the voltage dimension; This represents the uncertainty situation in the sample space; In the case of Under the following voltage conditions; In the case of The set of conditions that allow for the lower voltage; in, The probability of meeting the subsystem requirements in the power dimension; In the case of Under the following power conditions; In the case of The set of conditions that allow for lower power; Overall energy correlation reliability Take the smaller of the two values ​​above: ， in, This indicates that the smaller of the two probabilities of satisfying voltage and power is taken as the conservative reliability assessment at the energy level.

3. The method as described in claim 1, characterized in that, The reliability of the information association The failure rate parameters are obtained by combining the failure rates of different failure levels. in, The failure rate corresponds to the extremely low failure level, in units of ; The failure rate corresponding to the low failure level; The failure rate corresponding to a medium failure level; Information correlation reliability Number of failure modes at each level The combined calculation is as follows: ， in, For comprehensive reliability at the information level; The number of failure modes classified as extremely low failure levels; The number of failure modes classified as low failure levels; The number of failure modes classified as medium failure level; , , These represent the combined index terms for each level of failure rate.

4. The method as described in claim 1, characterized in that, The importance of causality Based on the impact of each subsystem on the overall reliability reduction Normalization yields the following, defined as: ， in, For the first Causal importance of subsystems; For the first The total decrease in the functional reliability of all subsystems caused by a decrease in the material-related reliability of a subsystem; For all subsystems The maximum value is used for normalization; This indicates that the influence quantity is normalized to The interval is used to obtain the relative importance.

5. The method as described in claim 4, characterized in that, When calculating the importance of causality, the first step is to set the... The subsystem at time New material correlation reliability For the original reliability of ,Right now: ， in, For at any time After reduction Subsystem material correlation reliability; For at any time Material correlation reliability without reduction; The preset reliability reduction factor is greater than ; Define the first by the following formula Subsystem and a certain reference subsystem relative change factor : ， in, For the first Subsystem relative to reference subsystem The relative change factor; In the first The average performance margin of the subsystem is from Reduced to Reliability of new material correlations under certain circumstances; For the reference subsystem The average performance margin is from Downgraded to Reliability of new material correlations under certain circumstances; For the first Mean performance margin of subsystem; This represents the average performance margin of the baseline subsystem.

6. The method as described in claim 1, characterized in that, The optimization objective The solution employs particle swarm optimization, genetic algorithm, or other swarm intelligence optimization algorithms, and satisfies... as well as The solution set is taken as the feasible region.

7. The method as described in claim 6, characterized in that, When using the particle swarm optimization algorithm, the vectors representing the mean and variance of the performance margin of each subsystem are used as the positions of the particles, and the objective function is applied. The calculated value is used as the particle fitness. Within the feasible region that satisfies the performance margin and system reliability constraints, the particle velocity and position are updated based on individual optimality and swarm optimality. The process is iterated until convergence to obtain an approximate optimal allocation scheme for the performance margin and reliability index of each subsystem.

8. A reliability index allocation system for a turnout tamping machine used to implement the method as described in any one of claims 1-7, characterized in that, The system includes: The data acquisition and modeling module is used to acquire the structural information, functional requirements and key performance parameters of each subsystem of the turnout tamping machine, convert the key performance parameters into normalized performance margins, establish a function-performance-margin model, and construct energy network and information network to form a material-energy-information ternary correlation model. The correlation reliability calculation module is used to calculate the energy correlation reliability and information correlation reliability of each subsystem based on the energy network and information network, and to calculate the material correlation reliability of each subsystem based on the performance margin uncertainty, thereby obtaining the functional reliability of each subsystem; The causal importance analysis module is used to calculate the overall reliability decrease and normalize it under the ternary correlation model by reducing the material correlation reliability of the target subsystem and comparing the functional reliability of each subsystem before and after the adjustment, thereby obtaining the causal importance of each subsystem. The reliability allocation optimization module is used to construct a reliability index allocation optimization model based on causal importance and functional reliability. Under the constraints of performance margin and system reliability, it uses the particle swarm optimization algorithm to solve the problem and outputs the allocation results of performance margin and reliability index for each subsystem.

9. A computer device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the method according to any one of claims 1-6.

10. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the method of any one of claims 1-6.

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